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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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and predict how the immune system responds to interventions. This tight integration of advanced machine learning and experimental immunology allows us to tackle fundamental biological questions with
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an advantage: plasma surface functionalization; electrode/electrolyte interfaces; battery degradation modelling; microstructure-resolved modelling; tomography or image-based electrode modelling; machine learning
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strategies within it. You will combine archaeological and landscape data (maps, satellite imagery, archaeological datasets) with machine learning approaches to build a system that highlights promising
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vision, engineering or related disciplines. Proficiency in English communication skills (oral and written) is expected for international collaboration. This position falls under a program promoting
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machine learning approaches to build a system that highlights promising locations for archaeological research. You are also expected to play an active role in project coordination and in writing follow-up
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-level modelling of environmental exposures and health risks; Interpretable and uncertainty-aware machine learning for heterogeneous health data. This position offers the opportunity to work in a
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expect the candidate to publish in competitive machine learning, software engineering and security venues, such as Neurips, ICML, ICLR, ICSE, FSE, ASE, Security & Privacy, USENIX Security, CCS, etc. More
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technology Technology » Dating techniques Technology » Information technology Researcher Profile Recognised Researcher (R2) Application Deadline 31 Aug 2026 - 23:59 (UTC) Country Belgium Type of Contract To be
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Industrial Engineering and Operations Research. Good technical knowledge of probabilistic models and machine learning Basic knowledge of quality management in the manufacturing industry (e.g., SPC, FMEA, HACCP